It would be surprising to me if the algorithm really performed this poorly on fashion mnist. F-MNIST is harder, but (intentionally) very similar to MNIST.
CIFAR maybe with limited categories would be a logical “hard” test IF it can be made to work on F-MNIST.
On the other hand (without claiming that I understand the ins and outs of the algorithm) I could imagine that out of the neuroinspired playbook it misses the winner-takes-all competition between neurons that allows modelling of multi-modal distribution and possibly allows easier distinction of not-linearly-separable datapoints.
See my comment on reversing the shades on F-MNIST. I will check it later but I see it gets up to 48% in the ‘wrong’ order and that is surprisingly good. I worked on CIFAR, but that is another story. As-is it gives bad results and you have to add other ‘things’.
As you guessed, I belong to neuroinspired branch and most of my ‘giants’ belong there. I strongly expected, when I started my investigations, to use some of the works that I knew and appreciated along the lines your are mentioning, and I investigated some of them early on.
To my surprise, I did not need them to get to this result, so they are absent.
The two neuronal layers form of the neocortex is where they will be useful. This is only one layer.
Another (bad) reason, is that they add to the GPU hell… that has limited my investigations. It is identified source of potential improvements.
It would be surprising to me if the algorithm really performed this poorly on fashion mnist. F-MNIST is harder, but (intentionally) very similar to MNIST.
CIFAR maybe with limited categories would be a logical “hard” test IF it can be made to work on F-MNIST.
On the other hand (without claiming that I understand the ins and outs of the algorithm) I could imagine that out of the neuroinspired playbook it misses the winner-takes-all competition between neurons that allows modelling of multi-modal distribution and possibly allows easier distinction of not-linearly-separable datapoints.
See my comment on reversing the shades on F-MNIST. I will check it later but I see it gets up to 48% in the ‘wrong’ order and that is surprisingly good. I worked on CIFAR, but that is another story. As-is it gives bad results and you have to add other ‘things’.
As you guessed, I belong to neuroinspired branch and most of my ‘giants’ belong there. I strongly expected, when I started my investigations, to use some of the works that I knew and appreciated along the lines your are mentioning, and I investigated some of them early on.
To my surprise, I did not need them to get to this result, so they are absent.
The two neuronal layers form of the neocortex is where they will be useful. This is only one layer.
Another (bad) reason, is that they add to the GPU hell… that has limited my investigations. It is identified source of potential improvements.